Microscopic hyperspectral image segmentation faces dual challenges of limited labeled data and insufficient utilization of unlabeled data. However, existing semi-supervised methods often isolate the training processes for labeled and unlabeled data, neglecting their potential synergistic effects. To address this, we propose a semi-supervised method based on Virtual Domain Collaborative Learning (VDCL) to enhance the collaborative learning ability between labeled and unlabeled data and improve the quality of pseudo-labels. Specifically, by combining unlabeled background with labeled foreground and labeled background with unlabeled foreground to construct virtual domain data pairs, we established a collaborative learning bridge between labeled and unlabeled samples. Furthermore, we establish a repository of optimal models and employ an alternating co-training strategy. The current and historically optimal models jointly guide training, and this dynamic framework significantly improves pseudo-labels quality. We have verified the novel semi-supervised segmentation method on the widely-used public microscopic hyperspectral choledoch dataset from Kaggle and the oral squamous cell carcinoma dataset. On these datasets, our method has achieved the state-of-the-art performance. The code is available at https://github.com/Qugeryolo/Virual-Domain .