In computer-assisted vascular surgery, real-time multi-instrument segmentation serves as a pre-requisite step. However, a large amount of effort has been dedicated to single-instrument rather than multi-instrument in computer-assisted intervention research to this day. To fill the overlooked gap, this study introduces a Light-Weight Deep Feature Refinement Network (DFR-Net) based on multi-task learning for real-time multi-instrument segmentation. In this network, the proposed feature refinement module (FRM) can capture long-term dependencies while retaining precise positional information, which helps the model locate the foreground objects of interest. The designed channel calibration module (CCM) can re-calibrate fusion weights of multi-level features, which helps the model balance the importance of semantic information and appearance information. Besides, the connectivity loss function is developed to address fractures in the wire-like structure segmentation results. Extensive experiments on two different types of datasets consistently demonstrate that DFR-Net can achieve state-of-the-art segmentation performance while meeting the real-time requirements.