A reliable, hard-landmark-sensitive loss is urgently needed in the field of heatmap-based facial landmark detection, as existing standard regression losses are ineffective at capturing small errors caused by peak mismatches and struggle to adaptively focus on hard-to-detect landmarks. These limitations potentially result in misguided model training, impacting both the coverage and accuracy of the model. To this end, we propose a novel POsition-aware and Sample-Sensitive Loss, named PossLoss, for reliable, hard-landmark sensitive landmark detection. Specifically, our PossLoss is position-aware, incorporating relative positional information to accurately differentiate and locate the peak of the heatmap, while adaptively balancing the influence of landmarks and background pixels through self-weighting, addressing the extreme imbalance between landmarks and non-landmarks. More advanced is that our PossLoss is sample-sensitive, which can distinguish easy and hard landmarks and adaptively make the model focused more on hard landmarks. Moreover, it addresses the difficulty of accurately evaluating heatmap distribution, especially in addressing small errors due to peak mismatches. We analyzed and evaluated our PossLoss on three challenging facial landmark detection tasks. The experimental results show that our PossLoss significantly improves the performance of landmark detection and outperforms the state-of-the-art methods. The source code will be made available on GitHub.