Crack segmentation plays a crucial role in assessing pavement technical conditions. The irregular tubular shape of cracks makes segmentation a challenging task. Dynamic snake convolution (DSConv) enhances feature extraction of tubular structures through deformation, but it comes with significant computational overhead. Through an analysis of the offset of the convolutional kernel in DSConv, we observe that the offsets tend to cluster around their mean values. To achieve high segmentation accuracy without introducing significant computational overhead, an optimized dynamic snake convolution module (ODSCM) is proposed for constructing a crack segmentation network. Instead of applying DSConv to all feature channels, the proposed ODSCM replaces DSConv with horizontal convolution (HC) and vertical convolution (VC) only on some channels. To compensate for the decrease in adaptive capability caused by this strategy, an offsets-guided feature optimization module (OFOM) is proposed, which optimizes the features extracted by HCs and VCs through the guidance of the offset of the convolutional kernel in DSConv. Comprehensive experiments are conducted on three datasets: Deepcrack, CrackLS315, and CFD. The results demonstrate that our method surpasses several state-of-the-art methods. The data and source code will be made public at https://github.com/name191/DSCCSNet .