Kalman Filter Estimate vs ACF Least Squares Estimate
As noted on p. 11 of this site: http://www.pages.drexel.edu/~pyo22/mem640/lecture04-Estimation/mem640Lecture-Estimation.pdf  least squares is a "special" case of Kalman Filtering. Additional details can also be found on other web sites, such as: http://www.insidegnss.com/node/3445

Also, as noted in both links above - although least squares is a special case of Kalman Filtering - the two algorithms differ from each other.  Note that the 'weighting matrix' W reduces to the identity matrix in the least squares case.  So you might expect that the results would also differ when you process the data.

As to which one will be more accurate - it all depends on how closely the 'signal processing model' of your algorithm matches the process that generates your input data.  I've programmed Kalman Filters whose results were completely wrong because: 1) it started with a bad first guess, or 2) the state model being used was wrong, or 3) the input data was far too noisy.  I've also programmed 'hypothesis testing' arrays of Kalman Filters, where all filters operated on the same data, but each filter was set up to match a particular target motion (ie: zig right, zig left, go straight, etc.) - in that case, only the filter that closely predicted the actual input data was the correct one (ie: residuals were very small), and the residuals of all the others were way out of range.