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Semantic segmentation is an important task in computer vision that is often tackled with convolutional neural networks (CNNs). A CNN learns to produce pixel-level predictions through training on pairs of images and their corresponding ground-truth segmentation labels. For segmentation tasks with multiple classes, the standard approach is to use a network that computes a multi-channel probabilistic segmentation map, with each channel representing one class. In applications where the image grid size (e.g., when it is a 3D volume) and/or the number of labels is relatively large, the standard (baseline) approach can become prohibitively expensive for our computational resources. In this paper, we propose a simple yet effective method to address this challenge. In our approach, the segmentation network produces a single-channel output, while being conditioned on a single class label, which determines the output class of the network. Our method, called label conditioned segmentation (LCS), can be used to segment images with a very large number of classes, which might be infeasible for the baseline approach. We also demonstrate in the experiments that label conditioning can improve the accuracy of a given backbone architecture, likely, thanks to its parameter efficiency. Finally, as we show in our results, an LCS model can produce previously unseen fine-grained labels during inference time, when only coarse labels were available during training. We provide all of our code here: https://github.com/tym002/Label-conditioned-segmentation