  Deep learning-based medical image segmentation is an essential yet
challenging task in clinical practice, which arises from restricted access to
annotated data coupled with the occurrence of domain shifts. Previous attempts
have focused on isolated solutions, while disregarding their
inter-connectedness. In this paper, we rethink the relationship between
semi-supervised learning (SSL) and domain generalization (DG), which are the
cutting-edge approaches to address the annotated data-driven constraints and
the domain shift issues. Inspired by class-level representation, we show that
unseen target data can be represented by a linear combination of source data,
which can be achieved by simple data augmentation. The augmented data enrich
domain distributions while having semantic consistency, aligning with the
principles of consistency-based SSL. Accordingly, we propose SSL-DG, fusing DG
and SSL, to achieve cross-domain generalization with limited annotations.
Specifically, the global and focal region augmentation, together with an
augmentation scale-balancing mechanism, are used to construct a mask-based
domain diffusion augmentation module to significantly enrich domain diversity.
In order to obtain consistent predictions for the same source data in different
networks, we use uncertainty estimation and a deep mutual learning strategy to
enforce the consistent constraint. Extensive experiments including ablation
studies are designed to validate the proposed SSL-DG. The results demonstrate
that our SSL-DG significantly outperforms state-of-the-art solutions in two
challenging DG tasks with limited annotations. Code is available at
https://github.com/yezanting/SSL-DG.
