The automatic segmentation of dental images plays a crucial role in the auxiliary diagnosis of oral diseases. To address the issues of large parameter sizes in existing segmentation models and low segmentation accuracy of medical dental images, a lightweight dental image segmentation model, namely, the quadrant oblique displacement (QOD) UNeXt is proposed. First, QOD blocks are designed to displace features along four oblique directions, that is, the upper-left, upper-right, lower-left, and lower-right, to diffuse features and dynamically aggregate tokens, which thereby enhances segmentation accuracy. Second, a localized feature integration (LFI) module is incorporated into the decoder to improve the ability of the model to integrate detailed and global information. Finally, an efficient channel attention (ECA) module is introduced at the skip connections to further fuse local and global features. Experimental results on the STS-MICCAI 2023 and Tufts public datasets demonstrate that QOD-UNeXt significantly improves segmentation accuracy while maintaining a lightweight structure. Therefore, QOD-UNeXt exhibits excellent performance in dental medical image segmentation tasks.