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Despite its effectiveness, insufficient diversity between the collaborating models often causes them to become highly consistent at an early stage, weakening the benefit of mutual learning and eventually making the framework behave similarly to self\u2010training. In an effort to postpone this premature convergence, different initial perturbations were incorporated, but unfortunately, this resulted in a decrease in the quality of the model's pseudo\u2010labels, which further deteriorated the model's performance. To tackle this issue, we propose an Uncertainty Aware and Diverse Co\u2010training model (UADC) which is in line with the essential postulates of co\u2010training. The model features a novel co\u2010training framework working in hybrid spatial\u2010frequency domain. Specifically, after revisiting the basic hypotheses of co\u2010training, we devise a novel multi\u2010view training approach that addresses the issues of dependency and role inequality inherent in traditional methods. Targeting the misconceptions in previous co\u2010training architectures, we incorporate Segformer and frequency domain training to decouple and thereby counteract the tendency towards self\u2010training degeneration during later training stages. Furthermore, to enhance the quality of pseudo\u2010labels, we delve into the impact of uncertainty on model performance. We alter the structure of the model, designing a method grounded in the epistemic uncertainty of the segmentation model to optimize the quality of pseudo labels. We conduct extensive experiments on three public medical datasets of ISIC, Kvasir and CVC\u2010ClinicDB, and compared our model with other state\u2010of\u2010the\u2010art approaches, demonstrating its effectiveness and competitive performance.<\/jats:p>","DOI":"10.1111\/exsy.70298","type":"journal-article","created":{"date-parts":[[2026,5,28]],"date-time":"2026-05-28T10:04:33Z","timestamp":1779962673000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["<scp>UADC<\/scp>\n                    : Uncertainty\u2010Aware Diverse Co\u2010Training for Semi\u2010Supervised Medical Image Segmentation"],"prefix":"10.1111","volume":"43","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0692-0815","authenticated-orcid":false,"given":"Tengfei","family":"Zhao","sequence":"first","affiliation":[{"name":"School of Chemical Process Automation Shenyang University of Technology  Shenyang China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Weiyan","family":"Tong","sequence":"additional","affiliation":[{"name":"School of Chemical Process Automation Shenyang University of Technology  Shenyang China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhixiao","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Computer Science and Engineering Northeastern University  Shenyang China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4549-744X","authenticated-orcid":false,"given":"Chong","family":"Fu","sequence":"additional","affiliation":[{"name":"School of Computer Science and Engineering Northeastern University  Shenyang China"},{"name":"Engineering Research Center of Security Technology of Complex Network System Ministry of Education  Shenyang China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Cheng","family":"Gao","sequence":"additional","affiliation":[{"name":"School of Chemical Process Automation Shenyang University of Technology  Shenyang China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2026,5,28]]},"reference":[{"key":"e_1_2_11_2_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.inffus.2021.05.008"},{"key":"e_1_2_11_3_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2023.103011"},{"key":"e_1_2_11_4_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.compmedimag.2015.02.007"},{"key":"e_1_2_11_5_1","doi-asserted-by":"publisher","DOI":"10.52202\/068431-2349"},{"key":"e_1_2_11_6_1","first-page":"2014","volume-title":"International Joint Conferences on Artificial Intelligence Organization","author":"Chen D.","year":"2018"},{"key":"e_1_2_11_7_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2022.106034"},{"key":"e_1_2_11_8_1","doi-asserted-by":"publisher","DOI":"10.1109\/TCBB.2023.3247433"},{"key":"e_1_2_11_9_1","doi-asserted-by":"publisher","DOI":"10.1109\/JSAC.2023.3310097"},{"key":"e_1_2_11_10_1","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2017.2699184"},{"key":"e_1_2_11_11_1","first-page":"2613","volume-title":"Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR)","author":"Chen X.","year":"2021"},{"key":"e_1_2_11_12_1","first-page":"168","volume-title":"Skin Lesion Analysis Toward Melanoma Detection: A Challenge at the 2017 International Symposium on Biomedical Imaging (Isbi), Hosted by the International Skin Imaging Collaboration (Isic)","author":"Codella N. 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