With the increasingly wide application of surgical robots in clinical practice, it is of great significance to provide doctors with precise semantic segmentation information of surgical instrument in endoscopic video to improve the clinicians’ operation accuracy and patients’ prognosis. Training surgical instrument segmentation models requires a large amount of accurately labeled video frames, which limits the application of deep learning in the surgical instrument segmentation task due to the high cost of video data labeling. The current semi-supervised methods enhance the temporal information and data diversity of sparsely labeled videos by predicting and interpolating frames, which can improve the segmentation accuracy with limited labeled data. However, these semi-supervised methods suffer from the drawbacks of frame interpolation quality and temporal feature extraction from sequential frames. To tackle this issue, this study proposes a semi-supervised segmentation framework with spatiotemporal Transformer, which can improve the temporal consistency and data diversity of sparsely labeled video datasets by interpolating frames with high accuracy and generating pseudo-labels. Here the Transformer module is integrated at the bottleneck position of the segmentation network to analyze global contextual information from both temporal and spatial perspectives, enhancing advanced semantic features while improving the perception to complex environments of the segmentation network, which can overcome various types of distractions in surgical videos and thus improve the segmentation effect. The proposed semi-supervised segmentation framework with Transformer achieves an average DICE of 82.42% and an average IOU of 72.01% on the MICCAI 2017 Surgical Instrument Segmentation Challenge dataset using only 30% labeled data, which exceeds the state-of-the-art method by 7.68% and 8.19%, respectively, and outperforms the fully supervised methods.