Learning from Uncertain Agreement and Informative Disagreement for Semi-Supervised 3D Medical Image Segmentation

Teacher–student learning is widely used for semi-supervised 3D medical image segmentation, yet effectively exploiting uncertain and conflicting teacher predictions remains challenging. Directly enforcing agreement may preserve ambiguity, whereas suppressing difficult regions can discard useful supervisory cues. To address this dilemma, we propose a reflective teacher–student framework that interprets teacher guidance according to predictive state and disagreement behavior. Specifically, Consensus Sharpening and Disagreement Reconciliation (CSDR) distinguishes uncertain agreement from informative disagreement and applies state-adaptive supervision to refine tentative consensus and reconcile meaningful conflicts. Teacher-Perturbation Disagreement Probing (TPDP) further characterizes disagreement through its responses to controlled teacher-probability perturbations and uses these responses to modulate teacher-guided learning. Experiments on BraTS2019, LA, and Pancreas-NIH demonstrate consistent improvements under limited-label settings, achieving the highest Dice score across all six evaluated labeling settings. The code will be publicly released upon acceptance.