  Recent advancements in self-supervised learning have unlocked the potential
to harness unlabeled data for auxiliary tasks, facilitating the learning of
beneficial priors. This has been particularly advantageous in fields like
medical image analysis, where labeled data are scarce. Although effective for
classification tasks, this methodology has shown limitations in more complex
applications, such as medical image segmentation. In this paper, we introduce
Medical imaging Enhanced with Dynamic Self-Adaptive Semantic Segmentation
(MedSASS), a dedicated self-supervised framework tailored for medical image
segmentation. We evaluate MedSASS against existing state-of-the-art methods
across four diverse medical datasets, showcasing its superiority. MedSASS
outperforms existing CNN-based self-supervised methods by 3.83% and matches the
performance of ViT-based methods. Furthermore, when MedSASS is trained
end-to-end, covering both encoder and decoder, it demonstrates significant
improvements of 14.4% for CNNs and 6% for ViT-based architectures compared to
existing state-of-the-art self-supervised strategies.
