Uncertainty-Guided Adaptive Correction for Semi-Supervised Medical Image Segmentation

Consistent perturbation strategies have emerged as a dominant paradigm in semi-supervised medical image segmentation. Nevertheless, prevailing approaches inadequately address two critical challenges: 1) prediction errors induced by data uncertainty from distribution shifts, and 2) loss instability caused by model uncertainty in parameter generalization. To overcome these limitations, we propose an Uncertainty-Guided Adaptive Correction (UGAC) framework with three key innovations. First, we develop a dual-path uncertainty rectification mechanism that employs normalized entropy measures to detect error-prone regions in unlabeled predictions, followed by bilateral correction through confidence-weighted fusion. Second, we introduce adversarial consistency constraints that leverage labeled data to discriminate authentic segmentation patterns, effectively regularizing uncertainty propagation in unlabeled predictions through spectral normalization. Third, we architect a frequency-aware segmentation backbone through our novel Freqfusion module, which performs adaptive spectral decomposition during feature decoding to explicitly disentangle high-frequency (boundary-aware) and low-frequency (structural) components, thereby enhancing anatomical boundary sensitivity. Comprehensive evaluations on MM-WHS, BUSI, M&Ms and PROMISE12 datasets demonstrate UGAC's superior performance. The proposed framework exhibits robust generalizability across CT, MRI, and ultrasound modalities, while achieving significantly lower computational complexity than baseline UNet implementations. The code will be available at https://github.com/SIGMACX/UGAC.