Your present measure of "heterogeneity" is presumably $1-R^2$ for the 2-sample case, so that if samples 1 and 2 agree for each category you have a result of 0, while if there is no correlation between the 2 samples with respect to category values (complete "heterogeneity") you have a result of 1.
The simplest way to extend this approach to multiple samples is to calculate the determinant of the correlation matrix. The correlation matrix displays the correlation coefficients for all pairs of the samples. The determinant of a covariance matrix can be used as a measure of overall dispersion; the correlation matrix removes the dependence of the covariance matrix on the scales of the variables, and restricts the values of the determinant to the interval [0,1]. For the 2-sample case this determinant is $1-R^2$, as you have been using. So for your first example (using R):
> mat1
sample1 sample2 sample3 sample4
A 0.5 0.8 0.5 0.5
B 0.3 0.5 0.9 0.2
C 0.1 0.9 0.1 0.6
D 0.7 0.1 0.3 0.3
E 0.7 0.8 0.3 0.5
the correlation matrix is:
> cor(mat1)
sample1 sample2 sample3 sample4
sample1 1.00000000 -0.4572127 -0.05057217 -0.2100420
sample2 -0.45721268 1.0000000 -0.23181527 0.7814038
sample3 -0.05057217 -0.2318153 1.00000000 -0.7624437
sample4 -0.21004201 0.7814038 -0.76244374 1.0000000
and its determinant is:
> det(cor(mat1))
[1] 0.02092142
You should, however, consider some of the limitations of this approach with data that represent proportions.
First, since proportions are limited to the range [0,1], measures of $R^2$ don't have the nice properties that you might expect from the usual presentations of correlations based on bivariate normal distributions. Depending on the nature of your data and how much you care about differences among proportions that are close to 0 or 1, you might consider a logit transformation of the proportions to spread them out over all real numbers (available directly as a function if you load the boot package in R):
$$\text{logit}(p)=\log\left(\frac{p}{1-p}\right),$$
although this will lead to problems if any of your proportions are exactly 0 or 1. Alternatively, you might consider a non-parametric correlation like Spearman's, available in R as cor(mat1,method="spearman"). That's basically the correlation coefficient calculated from the ranks of the values instead of from the values themselves.
Second, your should consider the danger in throwing away the information you have on the raw data underlying these proportions. If each proportion can be based on such a wide range of counts (1 thousand to 1 million) there might be some important relations of your samples or categories to the total number of counts that are important to understand, or the observed proportions might not be truly representative of the underlying phenomenon you think that you are measuring. A more complete statistical model that takes these possibilities into account could provide measures of "heterogeneity" that have a more solid basis.
