Accurate segmentation of 3D tubular structures in medical images is critical for clinical diagnosis and interventional planning. Although deep learning methods have advanced significantly, most existing approaches exhibit limited generalizability due to their reliance on structure-specific morphological priors. Consequently, these models are often constrained to particular anatomical regions-such as the colorectal tract, vasculature, or arteries-leading to suboptimal performance when applied across varied organ systems. Moreover, the joint modeling of global and local morphological characteristics remains underexplored in the context of tubular structure segmentation. To address these limitations, we propose MCDNet, a Morphological-Conditional Dual-view Network that integrates both contextual and morphological information through a target-adaptive Morphological-Conditional Convolution (MCConv). The network is composed of three sequential modules: (1) a morphological feature extraction stage leveraging MCConv to enhance structural sensitivity across diverse tubular geometries; (2) a contextual feature learning stage employing a cross-fusion mechanism that synergistically combines convolutional and attention-based representations; and (3) a residual self-attention fusion module that reinforces feature integration from decoupled morphological and contextual branches. We evaluate MCDNet on four benchmark datasets encompassing diverse tubular segmentation tasks. Experimental results demonstrate that MCDNet achieves superior performance over state-of-the-art methods, with an average Dice coefficient improvement of 6.93% and a 10.61% reduction in Hausdorff distance relative to a strong baseline. The source code is publicly available at https://github.com/wzydcg/MCDNet.