The segmentation of eyeball areas is a key step in medical ultrasound image processing and analysis. Since the eyeball ultrasound images collected by clinical equipment have disadvantages including noise interference, blurred areas, and similar edge gray levels, the existing methods cannot accurately segment eyeball areas. Therefore, this study proposes a semantic embedded attention mechanism for eyeball segmentation based on deformable convolutions. Firstly, deformable convolutions, instead of traditional convolutions, are used to improve the representational ability of the network in eyeball areas. Secondly, a semantic embedded attention mechanism is constructed to fuse semantic information among different layers, enhance the salient features in the target area, and reduce the wrong segmentation of the background area, thereby improving the segmentation accuracy of the network. Finally, in order to check the segmentation performance, the proposed model in this study is compared with three existing deep learning segmentation models, and it obtains the highest accuracy on the segmentation data set of ultrasound eyeball images, fully verifying that this model has better segmentation ability and robustness.