Uncertainty-Aware Multi-Branch Distillation for Label-Scarce Medical Image Segmentation

Semi-supervised learning (SSL) has gained attention in medical image segmentation by leveraging abundant unlabeled data to reduce reliance on expert annotations. However, existing methods underutilize the complementary information from diverse augmented views. This information is crucial for handling the inherent variability and subtle pathological changes in medical imaging. To address this issue, we introduce Mixstill, a novel dual-component model comprising Mask-Distillation and Uncertainty Estimation. Mixstill enforces cross-view consistency on multiple data streams under diverse perturbations, promoting robust feature learning that enhances segmentation accuracy under anatomical variability and subtle pathological variations. Building upon this cross-view consistency, we further introduce an uncertainty-aware fusion mechanism that adaptively combines predictions from different views based on their reliability, producing high-quality supervision signals for unlabeled data. Extensive experiments conducted on two public datasets demonstrate the effectiveness of the proposed method. Under scarce labeled data conditions, our approach achieves superior performance compared to state-of-the-art SSL methods.