DU-Net: Dual-level Uncertainty-Aware Few-Shot Medical Image Segmentation Via Evidential Deep Learning

Few-shot medical image segmentation (FSMIS) aims to achieve accurate segmentation with extremely limited labeled data. However, medical images are characterized by blurred boundaries, incomplete information, and high noise, leading to high uncertainty in model predictions. Most existing FSMIS methods, including recent descriptor-based state-of-the-art methods, generate segmentation results without explicitly modeling uncertainty at both the descriptor and prediction levels, which limits their reliability in complex regions. To address these problems, we propose DU-Net, a dual-level uncertainty-aware network designed to achieve reliable segmentation. Evidence extraction networks are incorporated to estimate the uncertainty of foreground and background descriptors. To suppress the interference of high-uncertainty descriptors, the descriptor-level uncertainty-aware similarity maps fusion module dynamically modulates the contribution of each descriptor during fusion. To enhance the prediction accuracy, our evidential dual-branch prediction module is designed to enable joint optimization of segmentation and evidential uncertainty estimation in a multi-task learning fashion. We impose consistency constraints on the predictions of the above two branches in low-uncertainty regions for mutual supervision. Experiments on three public medical image datasets demonstrate that our method outperforms current state-of-the-art methods.