Sampling-based path planning has been proven to be effective in solving high-dimensional path planning problems. In this paper, we present a target-biased heuristic strategy for sampling that efficiently guides the bidirectional Rapidly-exploring Random Tree (BiRRT) expansion. Our method computes a heuristic distance between nodes of the two trees and selects the pair with the minimum value to guide the tree expansion, thereby increasing the likelihood of connection and reducing the path length. Furthermore, we introduce an adaptive bias probability mechanism that dynamically adjusts the sampling behavior based on the evolving heuristic distance. Extensive experiments were conducted in various types of environments with different levels of obstacle density to demonstrate the effectiveness of the proposed method. The results show significant improvements in success rate, average path length, average number of nodes and average number of iterations compared to the baseline BiRRT.