Automatic hepatic vessel segmentation from computed tomography (CT) images is essential in computer-assisted liver surgery. However, because of the error-prone and time-consuming manual annotation, it is impractical to obtain the fully correct training labels of hepatic vein (HV) and highly branched portal vein (PV), which largely restricts the development of deep learning methods on hepatic vessel segmentation. To reduce the noise label interference, this paper builds a robust hepatic vessel segmentation model via analyzing the probability distribution relationship between noisy annotation labels and unobserved correct ones, and apply it to deep neural networks (DNNs). Meanwhile, for inferior vena cava (IVC) close to liver and PV in extrahepatic area, segmentation methods are also represented to enhance the completeness of hepatic vessel structure. Experiments, which are conducted on a public hepatic vessel dataset with noise interference, indicates that our model can decrease misclassified regions and increase the vessel recognition probability, simultaneously.