At present, the good performance of deep learning in medical image analysis mostly depends on high-quality labeled datasets. However, due to the professionalism and complexity of medical images, the labeling of datasets often requires huge costs. To tackle this problem, this study designs a semi-automatic labeling system based on deep active learning. This system reduces the number of labeled samples required for the training of the labeling model based on deep learning through the active learning algorithm, and the trained labeling model can be used for labeling the remaining dataset. The system is built on the basis of a Web application, which does not require installation and can be accessed across platforms. It is convenient for users to complete the labeling work.