Accurate brain tumor segmentation from MRI is limited by expensive annotations and data heterogeneity across scanners and sites. We propose a semi-supervised teacher-student framework that combines an uncertainty-aware pseudo-labeling teacher with a progressive, confidence-based curriculum for the student. The teacher produces probabilistic masks and per-pixel uncertainty; unlabeled scans are ranked by image-level confidence and introduced in stages, while a dual-loss objective trains the student to learn from high-confidence regions and unlearn low-confidence ones. Agreement-based refinement further improves pseudo-label quality. On BraTS 2021, validation DSC increased from 0.393 (10% data) to 0.872 (100%), with the largest gains in early stages, demonstrating data efficiency. The teacher reached a validation DSC of 0.922, and the student surpassed the teacher on tumor subregions (e.g., NCR/NET 0.797 and Edema 0.980); notably, the student recovered the Enhancing class (DSC 0.620) where the teacher failed. These results show that confidence-driven curricula and selective unlearning provide robust segmentation under limited supervision and noisy pseudo-labels.

翻译：MRI脑肿瘤精确分割受限于昂贵的标注成本以及跨扫描设备和采集站点的数据异质性。我们提出一种半监督师生框架，将不确定性感知的伪标注教师模型与基于置信度的渐进式课程学习机制相结合。教师模型生成概率掩码及逐像素不确定性；未标注扫描数据依据图像级置信度排序并分阶段引入，同时通过双损失目标训练学生模型，使其从高置信度区域学习并摒弃低置信度区域。基于一致性的伪标签优化策略进一步提升了伪标签质量。在BraTS 2021数据集上，验证集DSC从0.393（10%数据）提升至0.872（100%），且在早期阶段提升最为显著，证明了本方法的数据效率。教师模型达到0.922的验证DSC，而学生模型在肿瘤子区域（如NCR/NET 0.797和水肿区域0.980）表现超越教师；值得注意的是，学生模型在教师模型失效的增强区域（DSC 0.620）实现了有效恢复。这些结果表明，置信度驱动的课程学习与选择性遗忘机制能够在有限监督和噪声伪标签条件下提供鲁棒的分割性能。