Dental abnormality (DA) detection is of great significance to orthodontic treatment. However, it is difficult to detect abnormal teeth from the oral cavity due to the following problems: 1) the crowding dentition often overlaps with normal teeth and 2) the lesion regions are small on the tooth surface. To address such problems, a Category-Guide Attention U-Net (CGA-UNet) is proposed, where a deformable attention convolution (DAC) module is first devised to discriminate crowding teeth from normal ones by learning dentition spatial distribution information; then, a differential variable convolution (DVC) module is designed to perform pathological tooth identification by extracting the small lesion features; finally, an attentional feature fusion (AFF) module is developed to integrate the spatial information and lesion features to obtain the abnormal tooth region. Experiments conducted on the benchmark show excellent performance of CGA-UNet for DA detection, and it can further assist orthodontists in formulating orthodontic treatment plans.