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Current mainstream methods usually adopt two sub\u2010networks and encourage the two models to make consistent predictions for the same segmentation task through consistency regularization. However, the scarcity of medical samples reduces the effectiveness of consistency constraints, and this problem may be further exacerbated by the influence of noisy pseudo\u2010labels. In this work, we propose a novel co\u2010training framework based on dual diversity and pseudo\u2010label correction learning (DDPCL) to address these challenges. Specifically, firstly, we design a dual diversity learning strategy, in which data diversity fully mines the potential information of limited training samples through the CutMix operation, and feature diversity promotes the model to learn complementary feature representations by minimizing the similarity between the features extracted by the two sub\u2010networks. Secondly, we propose a pseudo\u2010label correction learning strategy, which regards the inconsistent region where the pseudo\u2010labels predicted by the two sub\u2010networks are different as potential bias regions, and guides the models to correct the bias in these regions. Extensive experiments on three public datasets (ACDC, LA and Pancreas\u2010NIH datasets) validate that the proposed method outperforms the state\u2010of\u2010the\u2010art semi\u2010supervised medical image segmentation. The code is available at <jats:ext-link xmlns:xlink=\"http:\/\/www.w3.org\/1999\/xlink\" xlink:href=\"http:\/\/github.com\/ddd0420\/ddpcl\">http:\/\/github.com\/ddd0420\/ddpcl<\/jats:ext-link>.<\/jats:p>","DOI":"10.1002\/ima.70194","type":"journal-article","created":{"date-parts":[[2025,9,3]],"date-time":"2025-09-03T12:22:47Z","timestamp":1756902167000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Dual Diversity and Pseudo\u2010Label Correction Learning for Semi\u2010Supervised Medical Image Segmentation"],"prefix":"10.1002","volume":"35","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5164-619X","authenticated-orcid":false,"given":"Guangxing","family":"Du","sequence":"first","affiliation":[{"name":"Sanya Science and Education Innovation Park Wuhan University of Technology  Sanya Hainan China"},{"name":"School of Computer Science and Artificial Intelligence Wuhan University of Technology  Wuhan Hubei 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