  Unavailability of large training datasets is a bottleneck that needs to be
overcome to realize the true potential of deep learning in histopathology
applications. Although slide digitization via whole slide imaging scanners has
increased the speed of data acquisition, labeling of virtual slides requires a
substantial time investment from pathologists. Eye gaze annotations have the
potential to speed up the slide labeling process. This work explores the
viability and timing comparisons of eye gaze labeling compared to conventional
manual labeling for training object detectors. Challenges associated with gaze
based labeling and methods to refine the coarse data annotations for subsequent
object detection are also discussed. Results demonstrate that gaze tracking
based labeling can save valuable pathologist time and delivers good performance
when employed for training a deep object detector. Using the task of
localization of Keratin Pearls in cases of oral squamous cell carcinoma as a
test case, we compare the performance gap between deep object detectors trained
using hand-labelled and gaze-labelled data. On average, compared to
`Bounding-box' based hand-labeling, gaze-labeling required $57.6\%$ less time
per label and compared to `Freehand' labeling, gaze-labeling required on
average $85\%$ less time per label.
