In the actual image segmentation tasks in the medical field, the phenomenon of limited labeled data accompanied by domain shifts often occurs and such domain shifts may exist in homologous or even heterologous data. In the study, a novel method was proposed to deal with this challenging phenomenon. Firstly, a model was trained with labeled data in source and target domains so as to adapt to unlabeled data. Then, the alignment at two main levels was realized. At the style level, based on multi-scale stylistic features, the alignment of unlabeled target images was maximized and unlabeled target image features were enhanced. At the inter-domain level, the similarity of the category centroids between target domain data and mixed image data was also maximized. Additionally, a fused supervised loss and alignment loss computation method was proposed. In validation experiments, two cross-domain medical image datasets were constructed: homologous and heterologous datasets. Experimental results showed that the proposed method had the more advantageous comprehensive performance than common semi-supervised and domain adaptation methods.