  Annotation scarcity has become a major obstacle for training powerful
deep-learning models for medical image segmentation, restricting their
deployment in clinical scenarios. To address it, semi-supervised learning by
exploiting abundant unlabeled data is highly desirable to boost the model
training. However, most existing works still focus on limited medical tasks and
underestimate the potential of learning across diverse tasks and multiple
datasets. Therefore, in this paper, we introduce a \textbf{Ver}satile
\textbf{Semi}-supervised framework (VerSemi) to point out a new perspective
that integrates various tasks into a unified model with a broad label space, to
exploit more unlabeled data for semi-supervised medical image segmentation.
Specifically, we introduce a dynamic task-prompted design to segment various
targets from different datasets. Next, this unified model is used to identify
the foreground regions from all labeled data, to capture cross-dataset
semantics. Particularly, we create a synthetic task with a cutmix strategy to
augment foreground targets within the expanded label space. To effectively
utilize unlabeled data, we introduce a consistency constraint. This involves
aligning aggregated predictions from various tasks with those from the
synthetic task, further guiding the model in accurately segmenting foreground
regions during training. We evaluated our VerSemi model on four public
benchmarking datasets. Extensive experiments demonstrated that VerSemi can
consistently outperform the second-best method by a large margin (e.g., an
average 2.69\% Dice gain on four datasets), setting new SOTA performance for
semi-supervised medical image segmentation. The code will be released.
