Tooth segmentation plays a crucial role in oral disease diagnosis, orthodontic treatment planning, and computer-aided diagnosis (CAD). However, the low-contrast and high-noise characteristics of cone-beam computed tomography (CBCT) images, along with blurred anatomical boundaries, weak semantic connectivity, and the lack of effective frequency-domain information, make accurate segmentation challenging for existing methods. To address this challenge, we propose DDWFNet, a multi-scale segmentation network that integrates dense connections with wavelet transform. The model features two key innovations: (1) The Dynamic Wavelet Transform Convolution (DWTC) module enhances feature representation in low-contrast, noisy regions through multi-scale frequency-domain analysis. (2) The Quaternary Wavelet Hybrid-subband Attention (QWHA) mechanism suppresses background noise and strengthens semantic connections through cross-scale feature fusion and adaptive weighting. We validated DDWFNet on both the 3D CBCT and ISIC-2018 datasets. The results show that DDWFNet improves the IoU by 5.99% and DSC by 3.62% on the 3D CBCT dataset, and increases IoU by 0.66% and DSC by 0.91% on the ISIC-2018 dataset compared to the state-of-the-art models. Our findings confirm that the combination of dense connectivity and wavelet transform in DDWFNet offers an innovative solution for high-precision dental image analysis.