Thin structures like vessels and neurons are crucial for many biomedical processes. Preserving their topology in the context of semantic segmentation, especially ensuring connectedness, is essential. Current image segmentation objectives, like Dice or cross-entropy losses, do not emphasize the correct topology but rather focus on volumetric overlap. This can result in disconnected structures negatively influencing downstream tasks like flow calculation. In this paper, we tackle this shortcoming by proposing a new loss function, specifically tailored towards thin structures, which we call Skeleton Recall Loss. It performs better or on par on four public datasets in comparison to the clDice Loss, a similar state-of-the-art approach for topology preservation, while requiring significantly less compute and memory.