Vision foundation models have demonstrated strong generalization in medical image segmentation by leveraging large-scale, heterogeneous pretraining. However, they often struggle to generalize to specialized clinical tasks under limited annotations or rare pathological variations, due to a mismatch between general priors and task-specific requirements. To address this, we propose Uncertainty-informed Collaborative Learning (UnCoL), a dual-teacher framework that harmonizes generalization and specialization in semi-supervised medical image segmentation. Specifically, UnCoL distills both visual and semantic representations from a frozen foundation model to transfer general knowledge, while concurrently maintaining a progressively adapting teacher to capture fine-grained and task-specific representations. To balance guidance from both teachers, pseudo-label learning in UnCoL is adaptively regulated by predictive uncertainty, which selectively suppresses unreliable supervision and stabilizes learning in ambiguous regions. Experiments on diverse 2D and 3D segmentation benchmarks show that UnCoL consistently outperforms state-of-the-art semi-supervised methods and foundation model baselines. Moreover, our model delivers near fully supervised performance with markedly reduced annotation requirements.

翻译：视觉基础模型通过利用大规模异构预训练，在医学图像分割中展现出强大的泛化能力。然而，由于通用先验知识与特定任务需求之间的不匹配，这些模型在标注有限或存在罕见病理变异的情况下，往往难以泛化至专业化的临床任务。为解决这一问题，我们提出了一种基于不确定性的协同学习框架，该双教师框架在半监督医学图像分割中协调了泛化与专业化。具体而言，UnCoL 从冻结的基础模型中蒸馏视觉与语义表征以迁移通用知识，同时维护一个渐进式适应的教师模型以捕获细粒度的任务特定表征。为平衡两位教师的指导，UnCoL 中的伪标签学习通过预测不确定性进行自适应调控，该机制选择性地抑制不可靠的监督并稳定模糊区域的学习。在多种 2D 与 3D 分割基准测试上的实验表明，UnCoL 始终优于当前最先进的半监督方法及基础模型基线。此外，我们的模型在显著降低标注需求的同时，实现了接近全监督的性能。