You are calculating a relative error for 0, which is already an indication of a problem. 0 can have only three relative error values, none useful for calculations: 0, -∞ and +∞.
Using absolute error provides datapoints that reveal a nice curve with two separate, tiny subgroups on the sides. Therefore you can use a piecewise approach. First few values, having no error, have correction of +0. Then there is the huge blue part that fits logarithmic curve with R²≈0.93. Then there is the problem with the final values (red): there is not enough of them to actually calculate any curve. The two options, the 2nd and 3rd order polynomials, give some reasonable correction, but I feel like they are going to fail as soon as you collect more data in that region. The blue part gets a better fit (R²≥0.96, not shown on the chart) if the values are moved to the logarithmic scale and higher-order polynomial (≥4) is used, but I am also expecting this to be overfitting for that particular data set.
But all the above make sense for that single, particular sample. If you need a general approach for your system, those fixed values will not work. While the middle, blue part seems like it may work with logarithmic curve for any sample, the green and the red ones will probably not fit the models I have chosen for them if you collect samples from a different specimen or under different conditions.
Also, if you are implementing this in a microcontroller with limited resources, consider approximating the logarithmic curve with straight sections. Calculating logarithms is expensive.