C3MT: Confidence-Calibrated Contrastive Mean Teacher for semi-supervised medical image segmentation

Semi-supervised learning is crucial for medical image segmentation due to the scarcity of labeled data. However, existing methods that combine consistency regularization and pseudo-labeling often suffer from inadequate feature representation, suboptimal subnetwork disagreement, and noisy pseudo-labels. To address these limitations, this paper proposed a novel Confidence-Calibrated Contrastive Mean Teacher (C3MT) framework. First, C3MT introduces a Contrastive Learning-based co-training strategy, where an adaptive disagreement adjustment mechanism dynamically regulates the divergence between student models. This not only preserves representation diversity but also stabilizes the training process. Second, C3MT introduces a Confidence-Calibrated and Category-Aligned uncertainty-guided region mixing strategy. The confidence-calibrated mechanism filters out unreliable pseudo-labels, whereas the category-aligned design restricts region swapping to patches of the same semantic category, preserving anatomical coherence and preventing semantic inconsistency in the mixed samples. Together, these components significantly enhance feature representation, training stability, and segmentation quality, especially in challenging low-annotation scenarios. Extensive experiments on ACDC, Synapse, and LA datasets show that C3MT consistently outperforms recent state-of-the-art methods. For example, on the ACDC dataset with 20% labeled data, C3MT achieves up to a 4.3% improvement in average Dice score and a reduction in HD95 of more than 1.0 mm compared with strong baselines. The implementation is publicly available at https://github.com/l1654485/C3MT.