DUCore: Dual Uncertainty-Guided Consistency and Regional Contrastive Learning for Semi-Supervised Medical Image Segmentation

Uncertainty-aware consistency learning is one of the reliable approaches in semi-supervised medical image segmentation, enforcing robust model predictions under various perturbations. However, existing methods often rely on multiple stochastic predictions or dual-network/decoder discrepancies to estimate uncertainty, which increases computational cost and discards uncertain regions, potentially missing complex structures such as ambiguous lesion boundaries. To address these challenges, we introduce a Dual Uncertainty-Guided Consistency and Regional Contrastive Learning (DUCore) framework. DUCore improves segmentation robustness by integrating two complementary loss functions within consistency learning. The dual uncertainty-guided consistency loss (DuCL) adaptively calibrates the prediction alignment by prioritizing uncertain regions. DuCL uses deterministic single-pass uncertainty estimation, employing entropy-based calibration for aleatoric uncertainty and Proxy Dirichlet calibration for epistemic uncertainty. These uncertainty measures are computed directly from network output, and moderately uncertain regions are weighted instead of being discarded, which preserves valuable learning signals. The Regional Contrastive Loss (ReCL) further refines feature separability using boundary- and gradient-based hard negative mining in the encoded representation space. By explicitly targeting structural ambiguities, ReCL distinguishes lesion and organ edges from visually similar boundary-adjacent regions and mitigates intensity overlaps in gradient-rich transitions. As a result, DUCore is able to delineate fine structures and complex boundaries with higher precision. Extensive experiments on various medical segmentation benchmarks reveal that DUCore outperforms existing consistency methods.