Automated program repair (APR) techniques are effective in fixing inevitable defects in software, enhancing development efficiency and software robustness. However, due to the difficulty of generating precise specifications, existing APR methods face two main challenges: generating too many plausible patch candidates and overfitting them to partial test cases. To tackle these challenges, we introduce a new APR method named PathFix, which leverages path-sensitive constraints extracted from correct execution paths to generate patches for repairing buggy code. It is based on one observation: if a buggy program is repairable, at least one expected path is supposed to replace the fault path in the patched program. PathFix operates in four main steps. First, it traces fault paths reaching the fault output in the buggy program. Second, it derives expected paths by analyzing the desired correct output on the control flow graph, where an expected path defines how a feasible patch leads to the correct execution. Third, PathFix generates and evaluates patches by solving state constraints along the expected path. Fourth, we validate the correctness of the generated patch. To further enhance repair performance and mitigate scalability issues introduced by path-sensitive analysis, we integrate a large language model (LLM) into our framework. Experimental results show that PathFix outperforms existing solutions, particularly in handling complex program structures such as loops and recursion.