We propose a data labeling tool that permits accurate labeling of images using less time and effort. Our tool, BUBL, uses a hexagonal grid with a variable size tiling for accurate labeling of object contours. The hexagonal lattice is superimposed by a bubble wrap interface in order to make the labeling task enjoyable. The resulting label mask is represented by a Gaussian kernel density estimator which provides accurate bounding contours, even for objects that include hollow regions. Furthermore, multiple annotations from different users are collected for every image, making it possible to "hint" a partial labeling so the user can finish labeling in less time. We show accuracy results by simulating the application of our labeling tool for the MSRC dataset and to a subset data set of Caltech-101.