{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,22]],"date-time":"2026-05-22T03:07:00Z","timestamp":1779419220843,"version":"3.53.1"},"publisher-location":"Cham","reference-count":32,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783032065926","type":"print"},{"value":"9783032065933","type":"electronic"}],"license":[{"start":{"date-parts":[[2025,9,29]],"date-time":"2025-09-29T00:00:00Z","timestamp":1759104000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,9,29]],"date-time":"2025-09-29T00:00:00Z","timestamp":1759104000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2026]]},"DOI":"10.1007\/978-3-032-06593-3_11","type":"book-chapter","created":{"date-parts":[[2025,9,28]],"date-time":"2025-09-28T12:37:54Z","timestamp":1759063074000},"page":"112-122","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["LEXU: Learning from\u00a0Expert Disagreement for\u00a0Single-Pass Uncertainty Estimation in\u00a0Medical Image Segmentation"],"prefix":"10.1007","author":[{"given":"Kudaibergen","family":"Abutalip","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Numan","family":"Saeed","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Fadillah","family":"Maani","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ikboljon","family":"Sobirov","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Vincent","family":"Andrearczyk","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Adrien","family":"Depeursinge","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mohammad","family":"Yaqub","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2025,9,29]]},"reference":[{"issue":"3","key":"11_CR1","doi-asserted-by":"publisher","first-page":"701","DOI":"10.1007\/s10792-016-0329-x","volume":"37","author":"A Almazroa","year":"2017","unstructured":"Almazroa, A., et al.: Agreement among ophthalmologists in marking the optic disc and optic cup in fundus images. Int. Ophthalmol. 37(3), 701\u2013717 (2017)","journal-title":"Int. Ophthalmol."},{"key":"11_CR2","doi-asserted-by":"crossref","unstructured":"Andrearczyk, V., et al.: Automatic head and neck tumor segmentation and outcome prediction relying on FDG-pet\/CT images: Findings from the second edition of the Hecktor challenge. Med. Image Anal. 90, 102972 (2023)","DOI":"10.1016\/j.media.2023.102972"},{"key":"11_CR3","unstructured":"Ashukha, A., Lyzhov, A., Molchanov, D., Vetrov, D.: Pitfalls of in-domain uncertainty estimation and Ensembling in deep learning (2021). https:\/\/arxiv.org\/abs\/2002.06470"},{"key":"11_CR4","unstructured":"Ayhan, M.S., Berens, P.: Test-time data augmentation for estimation of heteroscedastic aleatoric uncertainty in deep neural networks (2018). https:\/\/api.semanticscholar.org\/CorpusID:13998356"},{"key":"11_CR5","unstructured":"Baldock, R.J.N., Maennel, H., Neyshabur, B.: Deep learning through the lens of example difficulty. Adv. Neural Inf. Process. Syst. 34 (2021)"},{"issue":"12","key":"11_CR6","doi-asserted-by":"publisher","first-page":"2996","DOI":"10.1038\/s41591-023-02562-7","volume":"29","author":"CR Banerji","year":"2023","unstructured":"Banerji, C.R., Chakraborti, T., Harbron, C., MacArthur, B.D.: Clinical AI tools must convey predictive uncertainty for each individual patient. Nat. Med. 29(12), 2996\u20132998 (2023)","journal-title":"Nat. Med."},{"key":"11_CR7","unstructured":"Blundell, C., Cornebise, J., Kavukcuoglu, K., Wierstra, D.: Weight uncertainty in neural network. In: International Conference on Machine Learning (2015)"},{"key":"11_CR8","unstructured":"Gal, Y., Ghahramani, Z.: Dropout as a Bayesian approximation: representing model uncertainty in deep learning. In: Balcan, M.F., Weinberger, K.Q. (eds.) Proceedings of The 33rd ICML. Proceedings of Machine Learning Research, vol.\u00a048, pp. 1050\u20131059. PMLR, New York, New York, USA (2016)"},{"issue":"1","key":"11_CR9","doi-asserted-by":"publisher","first-page":"1513","DOI":"10.1007\/s10462-023-10562-9","volume":"56","author":"J Gawlikowski","year":"2023","unstructured":"Gawlikowski, J., et al.: A survey of uncertainty in deep neural networks. Artif. Intell. Rev. 56(1), 1513\u20131589 (2023)","journal-title":"Artif. Intell. Rev."},{"key":"11_CR10","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2021.102038","volume":"71","author":"C Gros","year":"2020","unstructured":"Gros, C., Lemay, A., Cohen-Adad, J.: Softseg: advantages of soft versus binary training for image segmentation. Med. Image Anal. 71, 102038 (2020)","journal-title":"Med. Image Anal."},{"key":"11_CR11","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on CVPR (2016)","DOI":"10.1109\/CVPR.2016.90"},{"key":"11_CR12","doi-asserted-by":"crossref","unstructured":"Jensen, M.H., J\u00f8rgensen, D.R., Jalaboi, R., Hansen, M.E., Olsen, M.A.: Improving uncertainty estimation in convolutional neural networks using inter-rater agreement. In: MICCAI, pp. 540\u2013548. Springer (2019)","DOI":"10.1007\/978-3-030-32251-9_59"},{"issue":"1","key":"11_CR13","first-page":"12341","volume":"3","author":"W Ji","year":"2021","unstructured":"Ji, W., et al.: Learning calibrated medical image segmentation via multi-rater agreement modeling. Proc. IEEE\/CVF Conf. CVPR 3(1), 12341\u201312351 (2021)","journal-title":"Proc. IEEE\/CVF Conf. CVPR"},{"issue":"3","key":"11_CR14","doi-asserted-by":"publisher","first-page":"1391","DOI":"10.1007\/s00330-018-5695-5","volume":"29","author":"L Joskowicz","year":"2019","unstructured":"Joskowicz, L., Cohen, D., Caplan, N., Sosna, J.: Inter-observer variability of manual contour delineation of structures in ct. Eur. Radiol. 29(3), 1391\u20131399 (2019)","journal-title":"Eur. Radiol."},{"key":"11_CR15","doi-asserted-by":"publisher","first-page":"54","DOI":"10.1016\/j.media.2018.08.006","volume":"50","author":"L Joskowicz","year":"2018","unstructured":"Joskowicz, L., Cohen, D., Caplan, N., Sosna, J.: Automatic segmentation variability estimation with segmentation priors. Med. Image Anal. 50, 54\u201364 (2018)","journal-title":"Med. Image Anal."},{"key":"11_CR16","doi-asserted-by":"crossref","unstructured":"Jungo, A., et al.: On the effect of inter-observer variability for a reliable estimation of uncertainty of medical image segmentation. In: MICCAI, pp. 682\u2013690. Springer (2018)","DOI":"10.1007\/978-3-030-00928-1_77"},{"key":"11_CR17","unstructured":"Kahl, K.C., L\u00fcth, C.T., Zenk, M., Maier-Hein, K., Jaeger, P.F.: Values: a framework for systematic validation of uncertainty estimation in semantic segmentation. arXiv preprint arXiv:2401.08501 (2024)"},{"issue":"3","key":"11_CR18","doi-asserted-by":"publisher","first-page":"237","DOI":"10.1111\/j.1442-9071.2007.01457.x","volume":"35","author":"S Kumar","year":"2007","unstructured":"Kumar, S., et al.: Glaucoma screening: analysis of conventional and telemedicine-friendly devices. Clin. Exp. Ophthalmol. 35(3), 237\u2013243 (2007)","journal-title":"Clin. Exp. Ophthalmol."},{"key":"11_CR19","doi-asserted-by":"crossref","unstructured":"Kushibar, K., Campello, V., Garrucho, L., Linardos, A., Radeva, P., Lekadir, K.: Layer ensembles: a single-pass uncertainty estimation in deep learning for segmentation. In: MICCAI, pp. 514\u2013524. Springer (2022)","DOI":"10.1007\/978-3-031-16452-1_49"},{"key":"11_CR20","unstructured":"Lakshminarayanan, B., Pritzel, A., Blundell, C.: Simple and scalable predictive uncertainty estimation using deep ensembles. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol.\u00a030. Curran Associates, Inc. (2017)"},{"key":"11_CR21","doi-asserted-by":"crossref","unstructured":"Lemay, A., Gros, C., Karthik, E., Cohen-Adad, J.: Label fusion and training methods for reliable representation of inter-rater uncertainty. Mach. Learn. Biomed. Imaging 1, 1\u201327 (01 2023)","DOI":"10.59275\/j.melba.2022-db5c"},{"key":"11_CR22","doi-asserted-by":"crossref","unstructured":"Liao, Z., Hu, S., Xie, Y., Xia, Y.: Modeling annotator preference and stochastic annotation error for medical image segmentation. Med. Image Anal. 92 (2021)","DOI":"10.1016\/j.media.2023.103028"},{"key":"11_CR23","doi-asserted-by":"crossref","unstructured":"Liao, Z., Hu, S., Xie, Y., Xia, Y.: Transformer-based annotation bias-aware medical image segmentation. In: MICCAI. Springer (2023)","DOI":"10.1007\/978-3-031-43901-8_3"},{"key":"11_CR24","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2023.102914","volume":"89","author":"HA Mehrtens","year":"2023","unstructured":"Mehrtens, H.A., Kurz, A., Bucher, T.C., Brinker, T.J.: Benchmarking common uncertainty estimation methods with histopathological images under domain shift and label noise. Med. Image Anal. 89, 102914 (2023)","journal-title":"Med. Image Anal."},{"key":"11_CR25","doi-asserted-by":"crossref","unstructured":"Mucs\u00e1nyi, B., Kirchhof, M., Oh, S.J.: Benchmarking uncertainty disentanglement: specialized uncertainties for specialized tasks. In: Globerson, A., et al., (eds.) Advances in NeurIPS, vol.\u00a037, pp. 50972\u201351038. Curran Associates, Inc. (2024)","DOI":"10.52202\/079017-1614"},{"key":"11_CR26","unstructured":"Myronenko, A., Siddiquee, M.M.R., Yang, D., He, Y., Xu, D.: Automated head and neck tumor segmentation from 3D pet\/CT (2022)"},{"issue":"1","key":"11_CR27","first-page":"1","volume":"69","author":"M Ng","year":"2022","unstructured":"Ng, M., et al.: Estimating uncertainty in neural networks for cardiac MRI segmentation: a benchmark study. IEEE Trans. Biomed. Eng. 69(1), 1\u201323 (2022)","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"11_CR28","first-page":"234","volume-title":"MICCAI","author":"O Ronneberger","year":"2015","unstructured":"Ronneberger, O., Fischer, P., Brox, T.: U-net: convolutional networks for biomedical image segmentation. In: Navab, N., Hornegger, J., Wells, W.M., Frangi, A.F. (eds.) MICCAI, pp. 234\u2013241. Springer International Publishing, Cham (2015)"},{"key":"11_CR29","doi-asserted-by":"crossref","unstructured":"Roshanzamir, P., et al.: How inter-rater variability relates to aleatoric and epistemic uncertainty: a case study with deep learning-based paraspinal muscle segmentation. In: UNSURE@MICCAI (2023)","DOI":"10.1007\/978-3-031-44336-7_8"},{"issue":"1","key":"11_CR30","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3359178","volume":"3","author":"M Schaekermann","year":"2019","unstructured":"Schaekermann, M., Beaton, G., Habib, M., Lim, A., Larson, K., Law, E.: Understanding expert disagreement in medical data analysis through structured adjudication. Proc. ACM on Human-Comput. Interaction 3(1), 1\u201323 (2019)","journal-title":"Proc. ACM on Human-Comput. Interaction"},{"issue":"6","key":"11_CR31","doi-asserted-by":"publisher","first-page":"2769","DOI":"10.1214\/009053607000000505","volume":"35","author":"GJ Sz\u00e9kely","year":"2007","unstructured":"Sz\u00e9kely, G.J., Rizzo, M.L., Bakirov, N.K.: Measuring and testing dependence by correlation of distances. Ann. Stat. 35(6), 2769\u20132794 (2007)","journal-title":"Ann. Stat."},{"issue":"1","key":"11_CR32","doi-asserted-by":"publisher","DOI":"10.1016\/j.metrad.2023.100003","volume":"1","author":"K Zou","year":"2023","unstructured":"Zou, K., Chen, Z., Yuan, X., Shen, X., Wang, M., Fu, H.: A review of uncertainty estimation and its application in medical imaging. Meta-Radio. 1(1), 100003 (2023)","journal-title":"Meta-Radio."}],"container-title":["Lecture Notes in Computer Science","Uncertainty for Safe Utilization of Machine Learning in Medical Imaging"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-032-06593-3_11","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,5,22]],"date-time":"2026-05-22T02:49:47Z","timestamp":1779418187000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-032-06593-3_11"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,9,29]]},"ISBN":["9783032065926","9783032065933"],"references-count":32,"URL":"https:\/\/doi.org\/10.1007\/978-3-032-06593-3_11","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,9,29]]},"assertion":[{"value":"29 September 2025","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"The authors have no competing interests to declare that are relevant to the content of this article.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Disclosure of Interests"}},{"value":"UNSURE","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Workshop on Uncertainty for Safe Utilization of Machine Learning in Medical Imaging","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Daejon","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Korea (Republic of)","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2025","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"27 September 2025","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"27 September 2025","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"7","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"unsure2025","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/unsuremiccai.github.io","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}