With the ever growing occurrences of skin cancer and limited healthcare settings, a reliable computer assisted diagnostic system is needed to assist the dermatologists for lesion diagnosis. Skin lesion segmentation on dermo- scopic images can be an efficient tool to determine the differences between benign and malignant skin lesions. The dermoscopic images in the public skin lesion datasets are collected from various sources around the world. The color of lesions in dermoscopic images can be strongly dependent on the light source. In this work, we provide a new insight on the effect of color constancy algorithms on skin lesion segmentation with deep learning algorithm. We pre-process the ISIC Challenge Segmentation 2017 dataset using different color constancy algorithms and study the effect on a popular semantic segmentation algorithm, i.e. Fully Convolutional Networks. We evaluate the results with two evaluation metrics, i.e. Dice Similarity Coefficient and Jaccard Similarity Index. Overall, our experiments showed improvements in semantic segmentation of skin lesions when pre-processed with color constancy algorithms. Further, we investigate the effect of these algorithms on different types of lesions (Naevi, Melanoma and Seborrhoeic Keratosis). We found pre-processing with color constancy algorithms improved the segmentation results on Naevi and Seborrhoeic Keratosis, but not Melanoma. Future work will seek to investigate an adaptive color constancy algorithm that could improve the segmentation results.