In recent years, due to the rapid development of artificial intelligence in the medical field, the demand for medical images from researchers has been increasing day by day. These medical images often need to be finely annotated before being put into use. Compared with natural images, the data annotation of medical images is more specialized and complex. Therefore, medical images face the problems of low annotation rate and high annotation cost, resulting in the scarcity of labeled samples. Fundus images, as an important medical image, can achieve the screening and primary diagnosis of most ophthalmic diseases such as diabetic retinopathy and glaucoma, but they also face some difficulty in annotation. To address this situation, this study designs and develops an efficient semi-automated annotation system for fundus images, which is innovative in that it can perform semi-automated annotation of multiple eye diseases. Various diseases are predicted based on the fundus images, and the types of prediction results include disease classification and lesion segmentation. The annotator only needs to review and modify the generated prediction results, and this process can greatly reduce the workload of the annotator. In addition, the system includes four modules: user management, project management, image management, and algorithm model management. These four modules enable task assignment in team annotation, visualization of annotation progress data, quick export of annotation results, and other user-friendly functions. The system greatly improves the annotation efficiency and experience of annotators.