Head-mounted eye tracking has significant potential for
mobile gaze-based interaction with ambient displays but current
interfaces lack information about the tracker\'s gaze estimation error.
Consequently, current interfaces do not exploit the full potential of
gaze input as the inherent estimation error can not be dealt with. The
error depends on the physical properties of the display and constantly
varies with changes in position and distance of the user to the display.
In this work we present a computational model of gaze estimation error
for head-mounted eye trackers. Our model covers the full processing
pipeline for mobile gaze estimation, namely mapping of pupil positions
to scene camera coordinates, marker-based display detection, and display
mapping. We build the model based on a series of controlled measurements
of a sample state-of-the-art monocular head-mounted eye tracker. Results
show that our model can predict gaze estimation error with a root mean
squared error of 17.99~px ($1.96^\\circ$).